Method and system for optimizing qualified rate of electrolytic refining high-purity indium product

The invention relates to a method and system for optimizing the qualified rate of an electrolytic refining high-purity indium product, and belongs to the field of quality control and optimization of high-purity metal products. And a statistical machine learning method is adopted to establish a high-purity indium product quality prediction model, the high-purity indium product quality prediction model is utilized to predict whether the high-purity indium product is qualified or not, and when the high-purity indium product is unqualified, the raw material component proportion and/or the electrolytic cell process parameters are optimized. According to the method, the qualified rate of the electrolytic refining high-purity indium product is optimized by utilizing a statistical machine learning method, and the stability of the electrolytic refining product is ensured. 本发明涉及一种优化电解精炼高纯铟产品合格率的方法及系统，属于高纯度金属产品质量控制和优化领域。采用统计机器学习方法建立高纯铟产品质量预测模型，利用高纯铟产品质量预测模型预测高纯铟产品是否合格，并在不合格时优化原料成分占比和/或电解槽工艺参数。本发明利用统计机器学习方法优化电解精炼高纯铟产品合格率